Predictive value of Alberta stroke program early CT score for perfusion weighted imaging - diffusion weighted imaging mismatch in stroke with middle cerebral artery occlusion
Bibliographic record
Abstract
This study aimed to quantitatively assess the consistency and correlation between perfusion weighted imaging (PWI)/ diffusion weighted imaging (DWI) Alberta Stroke Program Early CT Score (ASPECTS) mismatch and PWI/DWI mismatch.Sixty-eight acute ischemic stroke with middle cerebral artery occlusion who underwent magnetic resonance imaging before thrombectomy were eligible. DWI volume, PWI volume and PWI-DWI mismatch were measured. DWI-, PWI-, PWI-DWI ASPECTS were evaluated. Statistical analysis was performed to compare the correlation between volume and ASPECTS of DWI-, PWI- and PWI-DWI mismatch. Receiver operating characteristic curve analysis was used to assess the predictive value of the PWI-DWI ASPECTS mismatch for the occurrence of PWI-DWI mismatch in acute ischemic stroke patients with middle cerebral artery occlusion.Of 68 patients, the DWI volume, PWI volume and PWI-DWI mismatch volume were (27.76 ± 17.53) mL, (167.09 ± 59.64) mL and (139.33 ± 58.18) mL respectively. DWI-ASPECTS was 6.75 ± 1.90 with the interobserver agreement was κ=0.98 (95% CI, 0.95-0.99); PWI-ASPECTS was 3.09 ± 2.11 with the interobserver agreement was κ=0.95 (95% CI, 0.91-0.99); PWI-DWI ASPECTS mismatch was 6.75 ± 1.90. Spearman's rank correlation analysis revealed that PWI-DWI mismatch volume was negatively correlated with PWI-DWI ASPECTS mismatch (r = -0.802; P = .000). Receiver operating characteristic analysis showed that when the PWI-DWI ASPECTS mismatch cut point was ≥ 2, the under curve of PWI-DWI ASPECTS mismatch for predicting PWI-DWI mismatch was 0.954 (95%CI, 0.911-0.998), with the sensitivity and specificity were 84.00% and 100% respectively.PWI-DWI ASPECTS mismatch may represent a convenient surrogate for penumbra in clinical trials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".